# 课程网址为https://tianchi.aliyun.com/course/310/3555
# 逻辑回归的应用

import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression

# Step2:模型训练
## 构造数据集
x_fearures = np.array([[-1, -2], [-2, -1], [-3, -2], [1, 3], [2, 1], [3, 2]])
y_label = np.array([0, 0, 0, 1, 1, 1])
## 调用逻辑回归模型,用逻辑回归模型拟合构造的数据集
lr_clf = LogisticRegression().fit(x_fearures, y_label)  # fit()是根据给定的训练数据拟合模型,其拟合方程为 y=w0+w1*x1+w2*x2

# Step3:模型参数查看
## 查看其对应模型的w
print('the weight of Logistic Regression:', lr_clf.coef_)  # 广义线性模型的w1,w2...参数为coef_
## 查看其对应模型的w0
print('the intercept(w0) of Logistic Regression:', lr_clf.intercept_)  # 广义线性模型的w0参数为intercept_

# Step4:数据和模型可视化
## 可视化构造的数据样本点
plt.figure()
plt.scatter(x_fearures[:, 0], x_fearures[:, 1], c=y_label, s=50, cmap='viridis')
plt.title('Dataset')
plt.show()

# Step5:模型预测
# 可视化决策边界
plt.figure()
plt.scatter(x_fearures[:, 0], x_fearures[:, 1], c=y_label, s=50, cmap='viridis')
plt.title('Dataset')
nx, ny = 200, 100
x_min, x_max = plt.xlim()
y_min, y_max = plt.ylim()#xlim(),ylim()是获取或者是设定x,y轴的范围,不传参即为获取,传参即为设定
x_grid, y_grid = np.meshgrid(np.linspace(x_min, x_max, nx), np.linspace(y_min, y_max, ny))#np.meshgrid用来生成等差数列，参数分别为(开始值,结束值,样本数据量)
z_proba = lr_clf.predict_proba(np.c_[x_grid.ravel(), y_grid.ravel()])
#predict_proba()方法:进行概率估计,ravel()方法是将数组展开
z_proba = z_proba[:, 1].reshape(x_grid.shape)
plt.contour(x_grid, y_grid, z_proba, [0.5], linewidths=2., colors='blue')#plt.contour()是等高线?
plt.show()

### 可视化预测新样本
plt.figure()
## new point 1
x_fearures_new1 = np.array([[0, -1]])
plt.scatter(x_fearures_new1[:, 0], x_fearures_new1[:, 1], s=50, cmap='viridis')
plt.annotate(s='New point 1', xy=(0, -1), xytext=(-2, 0), color='blue',
             arrowprops=dict(arrowstyle='-|>', connectionstyle='arc3', color='red'))#plt.annotate()函数用于标注文字
## new point 2
x_fearures_new2 = np.array([[1, 2]])
plt.scatter(x_fearures_new2[:, 0], x_fearures_new2[:, 1], s=50, cmap='viridis')
plt.annotate(s='New point 2', xy=(1, 2), xytext=(-1.5, 2.5), color='red',
             arrowprops=dict(arrowstyle='-|>', connectionstyle='arc3', color='red'))
## 训练样本
plt.scatter(x_fearures[:, 0], x_fearures[:, 1], c=y_label, s=50, cmap='viridis')
plt.title('Dataset')
# 可视化决策边界
plt.contour(x_grid, y_grid, z_proba, [0.5], linewidths=2., colors='blue')
plt.show()

## 在训练集和测试集上分别利用训练好的模型进行预测
y_label_new1_predict = lr_clf.predict(x_fearures_new1)
y_label_new2_predict = lr_clf.predict(x_fearures_new2)#predict()方法输出预测分类
print('The New point 1 predict class:\n', y_label_new1_predict)
print('The New point 2 predict class:\n', y_label_new2_predict)
## 由于逻辑回归模型是概率预测模型（前文介绍的 p = p(y=1|x,\theta)）,所以我们可以利用 predict_proba 函数预测其概率
y_label_new1_predict_proba = lr_clf.predict_proba(x_fearures_new1)
y_label_new2_predict_proba = lr_clf.predict_proba(x_fearures_new2)
print('The New point 1 predict Probability of each class:\n', y_label_new1_predict_proba)
print('The New point 2 predict Probability of each class:\n', y_label_new2_predict_proba)

